{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import multiprocessing as mp\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sys.path.append(\"/home/users/huajiang.liu/codes/prediction\")\n",
    "# from behavior_prediction.format_obs_csv import format_obs_csv\n",
    "# from behavior_prediction.lane_change_feature_extractor import enum_info_of_each_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n",
      "haha [[24, 33, 42], [23, 31, 39]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World!\")\n",
    "def CalMulMatrix(mat1, mat2):\n",
    "    if len(mat1[0]) != len(mat2):\n",
    "        return 0\n",
    "    m = len(mat1)\n",
    "    n = len(mat1[0])\n",
    "    p = len(mat2[0])\n",
    "    res = [[0]*p for i in range(m)] #python深拷贝浅拷贝知识 res = [[0]*p ] *m会出错\n",
    "    for i in range(m):\n",
    "        for j in range(p):\n",
    "            res[i][j]=0\n",
    "            for k in range(n):\n",
    "                res[i][j] +=mat1[i][k]*mat2[k][j]\n",
    "    return res\n",
    "                \n",
    "    \n",
    "    \n",
    "if __name__ == \"__main__\":\n",
    "    mat1 = [[4,5],[3,5]]\n",
    "    mat2 = [[1,2,3],[4,5,6]]\n",
    "    res = CalMulMatrix(mat1, mat2)\n",
    "    print(\"haha\",res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'reshape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-b76eb3416adf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'reshape'"
     ]
    }
   ],
   "source": [
    "res = [[0,0,0],[0,0,0],[0,0,0]]\n",
    "res[1][2]=1\n",
    "res.reshape(-1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.exists(\"/mnt/data-3/huajiang.liu/BADCASE/output/obslc/20220304-121758_011_6_obslc.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20220304-121758_011_6_obslcdebug.csv'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "debug_pn = \"/mnt/data-3/huajiang.liu/BADCASE/output/obslc/20220304-121758_011_6_obslc.csv\"\n",
    "debug_pn.split(\"/\")[-1].split(\".\")[0]+\"debug.csv\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>stamp</th>\n",
       "      <th>filter reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>sad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  stamp filter reason\n",
       "0   1      3           sad\n",
       "1   1      3           sad"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [[1, 3, \"sad\"], [1, 3, \"sad\"]]\n",
    "# np.array(a)\n",
    "pd.DataFrame(a,columns=[\"id\", \"stamp\", \"filter reason\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['20220304-121758_011_6']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=debug_pn.split(\"/\")[-1]\n",
    "pattern = r\"[0-9]{8}-[0-9]{6}_[0-9]{3}_[0-9]{1}\"\n",
    "name = re.findall(pattern, x)\n",
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stamp</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1648693716189</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           stamp   id\n",
       "0  1648693716189  820"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/mnt/data-3/huajiang.liu/BADCASE/output/obslc/20220331-102602_182_6_obslc.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>5427</th>\n",
       "      <th>5428</th>\n",
       "      <th>5429</th>\n",
       "      <th>5430</th>\n",
       "      <th>5431</th>\n",
       "      <th>5432</th>\n",
       "      <th>5433</th>\n",
       "      <th>5434</th>\n",
       "      <th>5435</th>\n",
       "      <th>5436</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fpr</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.064179</td>\n",
       "      <td>0.064179</td>\n",
       "      <td>0.068445</td>\n",
       "      <td>0.068445</td>\n",
       "      <td>0.073424</td>\n",
       "      <td>0.073424</td>\n",
       "      <td>0.093154</td>\n",
       "      <td>0.093154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999685</td>\n",
       "      <td>0.999685</td>\n",
       "      <td>0.999780</td>\n",
       "      <td>0.999780</td>\n",
       "      <td>0.999937</td>\n",
       "      <td>0.999937</td>\n",
       "      <td>0.999969</td>\n",
       "      <td>0.999969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tpr</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>0.005376</td>\n",
       "      <td>0.005376</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.010753</td>\n",
       "      <td>...</td>\n",
       "      <td>0.864198</td>\n",
       "      <td>0.876543</td>\n",
       "      <td>0.876543</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.913580</td>\n",
       "      <td>0.913580</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thresholds</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.998301</td>\n",
       "      <td>0.998301</td>\n",
       "      <td>0.998171</td>\n",
       "      <td>0.998170</td>\n",
       "      <td>0.998031</td>\n",
       "      <td>0.998031</td>\n",
       "      <td>0.997484</td>\n",
       "      <td>0.997484</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024885</td>\n",
       "      <td>0.024857</td>\n",
       "      <td>0.023942</td>\n",
       "      <td>0.023937</td>\n",
       "      <td>0.020853</td>\n",
       "      <td>0.019991</td>\n",
       "      <td>0.019306</td>\n",
       "      <td>0.017561</td>\n",
       "      <td>0.017377</td>\n",
       "      <td>0.00889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 5437 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0        1         2         3         4         5         6  \\\n",
       "fpr         0.0  0.00001  0.064179  0.064179  0.068445  0.068445  0.073424   \n",
       "tpr         0.0  0.00000  0.000000  0.002688  0.002688  0.005376  0.005376   \n",
       "thresholds  2.0  1.00000  0.998301  0.998301  0.998171  0.998170  0.998031   \n",
       "\n",
       "                   7         8         9  ...      5427      5428      5429  \\\n",
       "fpr         0.073424  0.093154  0.093154  ...  0.999685  0.999685  0.999780   \n",
       "tpr         0.008065  0.008065  0.010753  ...  0.864198  0.876543  0.876543   \n",
       "thresholds  0.998031  0.997484  0.997484  ...  0.024885  0.024857  0.023942   \n",
       "\n",
       "                5430      5431      5432      5433      5434      5435  \\\n",
       "fpr         0.999780  0.999937  0.999937  0.999969  0.999969  1.000000   \n",
       "tpr         0.888889  0.888889  0.913580  0.913580  0.938272  0.938272   \n",
       "thresholds  0.023937  0.020853  0.019991  0.019306  0.017561  0.017377   \n",
       "\n",
       "               5436  \n",
       "fpr         1.00000  \n",
       "tpr         1.00000  \n",
       "thresholds  0.00889  \n",
       "\n",
       "[3 rows x 5437 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_roc = pd.read_csv(\"/mnt/data-3/huajiang.liu/BADCASE/Lucas/model/mod_v6_testroc.csv\",index_col=0)\n",
    "df_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fpr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-691cc7d066e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m threshold = np.interp(x=0.001,\n\u001b[0;32m----> 2\u001b[0;31m                       \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m                       fp=thresholds)\n\u001b[1;32m      4\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'fpr' is not defined"
     ]
    }
   ],
   "source": [
    "threshold = np.interp(x=0.001,\n",
    "                      xp=fpr,\n",
    "                      fp=thresholds)\n",
    "threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'thresholds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-e72767bf1653>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mthresholds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'thresholds' is not defined"
     ]
    }
   ],
   "source": [
    "thresholds.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00800561942834721 0.18466805877244044\n",
      "0.00800561942834721 0.184497454921792\n",
      "0.008008869326361578 0.15723940399610856\n",
      "0.008008869326361578 0.15690232145818306\n",
      "0.007996015575477577 0.11169411713942318\n",
      "0.007996015575477577 0.1116077203752352\n"
     ]
    }
   ],
   "source": [
    "for i,f in enumerate(fpr):\n",
    "    if np.abs(f-0.008) < 0.00001:\n",
    "        print(f,thresholds[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.9999995049243398\n",
      "0.1 0.008890327408165075\n",
      "0.2 0.008890327408165075\n",
      "0.30000000000000004 0.008890327408165075\n",
      "0.4 0.008890327408165075\n",
      "0.5 0.008890327408165075\n",
      "0.6000000000000001 0.008890327408165075\n",
      "0.7000000000000001 0.008890327408165075\n",
      "0.8 0.008890327408165075\n",
      "0.9 0.008890327408165075\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.008890327408165075"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = df_roc.loc[\"thresholds\"]\n",
    "t_mask = thresholds <= 1\n",
    "thresholds = thresholds[t_mask]\n",
    "fpr = df_roc.loc[\"fpr\"][t_mask]\n",
    "fpr = 1 - fpr\n",
    "tpr = df_roc.loc[\"tpr\"][t_mask]\n",
    "tpr = 1 - tpr\n",
    "\n",
    "plt.scatter(thresholds,fpr,s=1,c=\"y\")\n",
    "plt.scatter(thresholds,tpr,s=1,c=\"b\")\n",
    "# plt.scatter(fpr,tpr,s=1,c=\"black\")\n",
    "xs = [0.1*i for i in range(10)]\n",
    "plt.plot(0.889, 0.0255, 'x',c=\"r\")\n",
    "for x in xs:\n",
    "    threshold = np.interp(x=x,\n",
    "                      xp=fpr,\n",
    "                      fp=thresholds)\n",
    "    plt.plot(threshold, x, 'x',c=\"r\")\n",
    "    print(x,threshold )\n",
    "threshold\n",
    "# plt.scatter(tpr,thresholds,s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9993232832687861"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr = df_roc.loc[\"fpr\"]\n",
    "tpr = df_roc.loc[\"tpr\"]\n",
    "thresholds = df_roc.loc[\"thresholds\"]\n",
    "x = 0.025556690336225683\n",
    "threshold = np.interp(x=x,\n",
    "                      xp=fpr,\n",
    "                      fp=thresholds)\n",
    "plt.scatter(thresholds,fpr,s=1,c=\"y\")\n",
    "plt.scatter(thresholds,tpr,s=1,c=\"b\")\n",
    "plt.plot(threshold, x, 'x',c=\"r\")\n",
    "threshold\n",
    "# plt.scatter(tpr,thresholds,s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9994703709268448"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = np.interp(x=0.02,\n",
    "                      xp=fpr,\n",
    "                      fp=thresholds)\n",
    "threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999404194997848"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp = np.interp(x=0.0225,\n",
    "              xp=fpr,\n",
    "              fp=thresholds)\n",
    "fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999989705085753"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold1 = np.interp(x=0.001,\n",
    "                      xp=tpr,\n",
    "                      fp=thresholds)\n",
    "threshold1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  1  2\n",
       "a  1.0  2  3\n",
       "b  2.0  3  5\n",
       "c  0.1  2  4"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [[1,2,3],[2,3,5],[0.1,2,4]]\n",
    "b = [[1,2,3],[2,3,5],[0.1,2,4]]\n",
    "c=[[],[],[]]\n",
    "pd.DataFrame(a,index=[\"a\",\"b\",\"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1. , 2. , 3. ],\n",
       "        [2. , 3. , 5. ],\n",
       "        [0.1, 2. , 4. ]],\n",
       "\n",
       "       [[1. , 2. , 3. ],\n",
       "        [2. , 3. , 5. ],\n",
       "        [0.1, 2. , 4. ]]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.stack((a,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1. , 2. , 3. ],\n",
       "       [2. , 3. , 5. ],\n",
       "       [0.1, 2. , 4. ]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((a,c),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   score\n",
       "0      1\n",
       "1      2\n",
       "2      3\n",
       "3      4\n",
       "4      5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(a,columns=[\"score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/mnt/data-3/huajiang.liu/BADCASE/Lucas/model/output.csv\",index_col=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "              Unnamed: 0      id     socre  label\n",
       "stamp                                            \n",
       "1.646364e+12           0  1316.0  0.997383   True\n",
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       "1.646364e+12           3  1326.0  0.996324   True\n",
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       "1.646631e+12        8969  3203.0  0.991581   True\n",
       "\n",
       "[8970 rows x 4 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pack_plate',\n",
       " 'pack_date',\n",
       " 'pack_name',\n",
       " 'cluster_obs_fillback_csv_pn',\n",
       " 'cluster_lane_fillback_csv_pn',\n",
       " 'cluster_feature_csv_pn',\n",
       " 'cluster_obs_format_csv_pn',\n",
       " 'cluster_ego_csv_pn',\n",
       " 'cluster_gt_csv_pn',\n",
       " 'cluster_ego_lanechange_csv_pn',\n",
       " 'cluster_sample_csv_pn',\n",
       " 'sample_start_stamp',\n",
       " 'sample_end_stamp',\n",
       " 'sample_attr',\n",
       " 'context_frame',\n",
       " 'context_time',\n",
       " 'lead_time',\n",
       " 'max_stamp_interval',\n",
       " 'ego_lane_change_ignore_duration',\n",
       " 'lon_dist_min',\n",
       " 'lon_dist_max',\n",
       " 'mono_fov',\n",
       " 'stamp_common_diff',\n",
       " 'max_stamp_diff_th',\n",
       " 'stamp_0',\n",
       " 'id_0',\n",
       " 'uid_0',\n",
       " 'x_0',\n",
       " 'y_0',\n",
       " 'vx_0',\n",
       " 'vy_0',\n",
       " 'ax_0',\n",
       " 'ay_0',\n",
       " 'yaw_0',\n",
       " 'yaw_rate_0',\n",
       " 'length_0',\n",
       " 'width_0',\n",
       " 'bx1_0',\n",
       " 'by1_0',\n",
       " 'bx2_0',\n",
       " 'by2_0',\n",
       " 'bx3_0',\n",
       " 'by3_0',\n",
       " 'bx4_0',\n",
       " 'by4_0',\n",
       " 'image_left_0',\n",
       " 'image_top_0',\n",
       " 'image_right_0',\n",
       " 'image_bottom_0',\n",
       " 'property_type_0',\n",
       " 'property_name_0',\n",
       " 'turn_light_0',\n",
       " 'lat_dist_subobs_front_to_tarlaneline_0',\n",
       " 'lat_dist_subobs_rear_to_tarlaneline_0',\n",
       " 'lat_avg_dist_subobs_to_tarlaneline_0',\n",
       " 'lat_min_dist_subobs_to_tarlaneline_0',\n",
       " 'lat_dist_subobs_b1_to_tarlaneline_0',\n",
       " 'lat_dist_subobs_b2_to_tarlaneline_0',\n",
       " 'lat_dist_subobs_b3_to_tarlaneline_0',\n",
       " 'lat_dist_subobs_b4_to_tarlaneline_0',\n",
       " 'lon_dist_subobs_to_tarfrontobs_0',\n",
       " 'lon_dist_subobs_to_tarrearobs_0',\n",
       " 'lon_dist_subobs_to_frontobs_0',\n",
       " 'lon_dist_subobs_to_rearobs_0',\n",
       " 'ego_phy_x_0',\n",
       " 'ego_phy_y_0',\n",
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       " 'ego_phy_vy_0',\n",
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       " 'obs_phy_x_0',\n",
       " 'obs_phy_y_0',\n",
       " 'obs_phy_vx_0',\n",
       " 'obs_phy_vy_0',\n",
       " 'obs_phy_yaw_0',\n",
       " 'target_front_obs_trackid_0',\n",
       " 'target_front_obs_uid_0',\n",
       " 'target_front_obs_chassis_x_0',\n",
       " 'target_front_obs_chassis_y_0',\n",
       " 'target_front_obs_chassis_vx_0',\n",
       " 'target_front_obs_chassis_vy_0',\n",
       " 'target_front_obs_chassis_yaw_0',\n",
       " 'target_front_obs_chassis_bx1_0',\n",
       " 'target_front_obs_chassis_bx2_0',\n",
       " 'target_front_obs_chassis_bx3_0',\n",
       " 'target_front_obs_chassis_bx4_0',\n",
       " 'target_front_obs_chassis_by1_0',\n",
       " 'target_front_obs_chassis_by2_0',\n",
       " 'target_front_obs_chassis_by3_0',\n",
       " 'target_front_obs_chassis_by4_0',\n",
       " 'target_front_obs_length_0',\n",
       " 'target_front_obs_width_0',\n",
       " 'target_front_obs_phy_yaw_0',\n",
       " 'target_front_obs_phy_x_0',\n",
       " 'target_front_obs_phy_y_0',\n",
       " 'target_front_obs_phy_vx_0',\n",
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       " 'target_rear_obs_trackid_0',\n",
       " 'target_rear_obs_uid_0',\n",
       " 'target_rear_obs_chassis_x_0',\n",
       " 'target_rear_obs_chassis_y_0',\n",
       " 'target_rear_obs_chassis_vx_0',\n",
       " 'target_rear_obs_chassis_vy_0',\n",
       " 'target_rear_obs_chassis_yaw_0',\n",
       " 'target_rear_obs_chassis_bx1_0',\n",
       " 'target_rear_obs_chassis_bx2_0',\n",
       " 'target_rear_obs_chassis_bx3_0',\n",
       " 'target_rear_obs_chassis_bx4_0',\n",
       " 'target_rear_obs_chassis_by1_0',\n",
       " 'target_rear_obs_chassis_by2_0',\n",
       " 'target_rear_obs_chassis_by3_0',\n",
       " 'target_rear_obs_chassis_by4_0',\n",
       " 'target_rear_obs_length_0',\n",
       " 'target_rear_obs_width_0',\n",
       " 'target_rear_obs_phy_yaw_0',\n",
       " 'target_rear_obs_phy_x_0',\n",
       " 'target_rear_obs_phy_y_0',\n",
       " 'target_rear_obs_phy_vx_0',\n",
       " 'target_rear_obs_phy_vy_0',\n",
       " 'front_obs_trackid_0',\n",
       " 'front_obs_uid_0',\n",
       " 'front_obs_chassis_x_0',\n",
       " 'front_obs_chassis_y_0',\n",
       " 'front_obs_chassis_vx_0',\n",
       " 'front_obs_chassis_vy_0',\n",
       " 'front_obs_chassis_yaw_0',\n",
       " 'front_obs_chassis_bx1_0',\n",
       " 'front_obs_chassis_bx2_0',\n",
       " 'front_obs_chassis_bx3_0',\n",
       " 'front_obs_chassis_bx4_0',\n",
       " 'front_obs_chassis_by1_0',\n",
       " 'front_obs_chassis_by2_0',\n",
       " 'front_obs_chassis_by3_0',\n",
       " 'front_obs_chassis_by4_0',\n",
       " 'front_obs_length_0',\n",
       " 'front_obs_width_0',\n",
       " 'front_obs_phy_yaw_0',\n",
       " 'front_obs_phy_x_0',\n",
       " 'front_obs_phy_y_0',\n",
       " 'front_obs_phy_vx_0',\n",
       " 'front_obs_phy_vy_0',\n",
       " 'rear_obs_trackid_0',\n",
       " 'rear_obs_uid_0',\n",
       " 'rear_obs_chassis_x_0',\n",
       " 'rear_obs_chassis_y_0',\n",
       " 'rear_obs_chassis_vx_0',\n",
       " 'rear_obs_chassis_vy_0',\n",
       " 'rear_obs_chassis_yaw_0',\n",
       " 'rear_obs_chassis_bx1_0',\n",
       " 'rear_obs_chassis_bx2_0',\n",
       " 'rear_obs_chassis_bx3_0',\n",
       " 'rear_obs_chassis_bx4_0',\n",
       " 'rear_obs_chassis_by1_0',\n",
       " 'rear_obs_chassis_by2_0',\n",
       " 'rear_obs_chassis_by3_0',\n",
       " 'rear_obs_chassis_by4_0',\n",
       " 'rear_obs_length_0',\n",
       " 'rear_obs_width_0',\n",
       " 'rear_obs_phy_yaw_0',\n",
       " 'rear_obs_phy_x_0',\n",
       " 'rear_obs_phy_y_0',\n",
       " 'rear_obs_phy_vx_0',\n",
       " 'rear_obs_phy_vy_0',\n",
       " 'idx_in_lane_0',\n",
       " 'stamp_1',\n",
       " 'id_1',\n",
       " 'uid_1',\n",
       " 'x_1',\n",
       " 'y_1',\n",
       " 'vx_1',\n",
       " 'vy_1',\n",
       " 'ax_1',\n",
       " 'ay_1',\n",
       " 'yaw_1',\n",
       " 'yaw_rate_1',\n",
       " 'length_1',\n",
       " 'width_1',\n",
       " 'bx1_1',\n",
       " 'by1_1',\n",
       " 'bx2_1',\n",
       " 'by2_1',\n",
       " 'bx3_1',\n",
       " 'by3_1',\n",
       " 'bx4_1',\n",
       " 'by4_1',\n",
       " 'image_left_1',\n",
       " 'image_top_1',\n",
       " 'image_right_1',\n",
       " 'image_bottom_1',\n",
       " 'property_type_1',\n",
       " 'property_name_1',\n",
       " 'turn_light_1',\n",
       " 'lat_dist_subobs_front_to_tarlaneline_1',\n",
       " 'lat_dist_subobs_rear_to_tarlaneline_1',\n",
       " 'lat_avg_dist_subobs_to_tarlaneline_1',\n",
       " 'lat_min_dist_subobs_to_tarlaneline_1',\n",
       " 'lat_dist_subobs_b1_to_tarlaneline_1',\n",
       " 'lat_dist_subobs_b2_to_tarlaneline_1',\n",
       " 'lat_dist_subobs_b3_to_tarlaneline_1',\n",
       " 'lat_dist_subobs_b4_to_tarlaneline_1',\n",
       " 'lon_dist_subobs_to_tarfrontobs_1',\n",
       " 'lon_dist_subobs_to_tarrearobs_1',\n",
       " 'lon_dist_subobs_to_frontobs_1',\n",
       " 'lon_dist_subobs_to_rearobs_1',\n",
       " 'ego_phy_x_1',\n",
       " 'ego_phy_y_1',\n",
       " 'ego_phy_vx_1',\n",
       " 'ego_phy_vy_1',\n",
       " 'ego_phy_yaw_1',\n",
       " 'obs_phy_x_1',\n",
       " 'obs_phy_y_1',\n",
       " 'obs_phy_vx_1',\n",
       " 'obs_phy_vy_1',\n",
       " 'obs_phy_yaw_1',\n",
       " 'target_front_obs_trackid_1',\n",
       " 'target_front_obs_uid_1',\n",
       " 'target_front_obs_chassis_x_1',\n",
       " 'target_front_obs_chassis_y_1',\n",
       " 'target_front_obs_chassis_vx_1',\n",
       " 'target_front_obs_chassis_vy_1',\n",
       " 'target_front_obs_chassis_yaw_1',\n",
       " 'target_front_obs_chassis_bx1_1',\n",
       " 'target_front_obs_chassis_bx2_1',\n",
       " 'target_front_obs_chassis_bx3_1',\n",
       " 'target_front_obs_chassis_bx4_1',\n",
       " 'target_front_obs_chassis_by1_1',\n",
       " 'target_front_obs_chassis_by2_1',\n",
       " 'target_front_obs_chassis_by3_1',\n",
       " 'target_front_obs_chassis_by4_1',\n",
       " 'target_front_obs_length_1',\n",
       " 'target_front_obs_width_1',\n",
       " 'target_front_obs_phy_yaw_1',\n",
       " 'target_front_obs_phy_x_1',\n",
       " 'target_front_obs_phy_y_1',\n",
       " 'target_front_obs_phy_vx_1',\n",
       " 'target_front_obs_phy_vy_1',\n",
       " 'target_rear_obs_trackid_1',\n",
       " 'target_rear_obs_uid_1',\n",
       " 'target_rear_obs_chassis_x_1',\n",
       " 'target_rear_obs_chassis_y_1',\n",
       " 'target_rear_obs_chassis_vx_1',\n",
       " 'target_rear_obs_chassis_vy_1',\n",
       " 'target_rear_obs_chassis_yaw_1',\n",
       " 'target_rear_obs_chassis_bx1_1',\n",
       " 'target_rear_obs_chassis_bx2_1',\n",
       " 'target_rear_obs_chassis_bx3_1',\n",
       " 'target_rear_obs_chassis_bx4_1',\n",
       " 'target_rear_obs_chassis_by1_1',\n",
       " 'target_rear_obs_chassis_by2_1',\n",
       " 'target_rear_obs_chassis_by3_1',\n",
       " 'target_rear_obs_chassis_by4_1',\n",
       " 'target_rear_obs_length_1',\n",
       " 'target_rear_obs_width_1',\n",
       " 'target_rear_obs_phy_yaw_1',\n",
       " 'target_rear_obs_phy_x_1',\n",
       " 'target_rear_obs_phy_y_1',\n",
       " 'target_rear_obs_phy_vx_1',\n",
       " 'target_rear_obs_phy_vy_1',\n",
       " 'front_obs_trackid_1',\n",
       " 'front_obs_uid_1',\n",
       " 'front_obs_chassis_x_1',\n",
       " 'front_obs_chassis_y_1',\n",
       " 'front_obs_chassis_vx_1',\n",
       " 'front_obs_chassis_vy_1',\n",
       " 'front_obs_chassis_yaw_1',\n",
       " 'front_obs_chassis_bx1_1',\n",
       " 'front_obs_chassis_bx2_1',\n",
       " 'front_obs_chassis_bx3_1',\n",
       " 'front_obs_chassis_bx4_1',\n",
       " 'front_obs_chassis_by1_1',\n",
       " 'front_obs_chassis_by2_1',\n",
       " 'front_obs_chassis_by3_1',\n",
       " 'front_obs_chassis_by4_1',\n",
       " 'front_obs_length_1',\n",
       " 'front_obs_width_1',\n",
       " 'front_obs_phy_yaw_1',\n",
       " 'front_obs_phy_x_1',\n",
       " 'front_obs_phy_y_1',\n",
       " 'front_obs_phy_vx_1',\n",
       " 'front_obs_phy_vy_1',\n",
       " 'rear_obs_trackid_1',\n",
       " 'rear_obs_uid_1',\n",
       " 'rear_obs_chassis_x_1',\n",
       " 'rear_obs_chassis_y_1',\n",
       " 'rear_obs_chassis_vx_1',\n",
       " 'rear_obs_chassis_vy_1',\n",
       " 'rear_obs_chassis_yaw_1',\n",
       " 'rear_obs_chassis_bx1_1',\n",
       " 'rear_obs_chassis_bx2_1',\n",
       " 'rear_obs_chassis_bx3_1',\n",
       " 'rear_obs_chassis_bx4_1',\n",
       " 'rear_obs_chassis_by1_1',\n",
       " 'rear_obs_chassis_by2_1',\n",
       " 'rear_obs_chassis_by3_1',\n",
       " 'rear_obs_chassis_by4_1',\n",
       " 'rear_obs_length_1',\n",
       " 'rear_obs_width_1',\n",
       " 'rear_obs_phy_yaw_1',\n",
       " 'rear_obs_phy_x_1',\n",
       " 'rear_obs_phy_y_1',\n",
       " 'rear_obs_phy_vx_1',\n",
       " 'rear_obs_phy_vy_1',\n",
       " 'idx_in_lane_1',\n",
       " 'stamp_2',\n",
       " 'id_2',\n",
       " 'uid_2',\n",
       " 'x_2',\n",
       " 'y_2',\n",
       " 'vx_2',\n",
       " 'vy_2',\n",
       " 'ax_2',\n",
       " 'ay_2',\n",
       " 'yaw_2',\n",
       " 'yaw_rate_2',\n",
       " 'length_2',\n",
       " 'width_2',\n",
       " 'bx1_2',\n",
       " 'by1_2',\n",
       " 'bx2_2',\n",
       " 'by2_2',\n",
       " 'bx3_2',\n",
       " 'by3_2',\n",
       " 'bx4_2',\n",
       " 'by4_2',\n",
       " 'image_left_2',\n",
       " 'image_top_2',\n",
       " 'image_right_2',\n",
       " 'image_bottom_2',\n",
       " 'property_type_2',\n",
       " 'property_name_2',\n",
       " 'turn_light_2',\n",
       " 'lat_dist_subobs_front_to_tarlaneline_2',\n",
       " 'lat_dist_subobs_rear_to_tarlaneline_2',\n",
       " 'lat_avg_dist_subobs_to_tarlaneline_2',\n",
       " 'lat_min_dist_subobs_to_tarlaneline_2',\n",
       " 'lat_dist_subobs_b1_to_tarlaneline_2',\n",
       " 'lat_dist_subobs_b2_to_tarlaneline_2',\n",
       " 'lat_dist_subobs_b3_to_tarlaneline_2',\n",
       " 'lat_dist_subobs_b4_to_tarlaneline_2',\n",
       " 'lon_dist_subobs_to_tarfrontobs_2',\n",
       " 'lon_dist_subobs_to_tarrearobs_2',\n",
       " 'lon_dist_subobs_to_frontobs_2',\n",
       " 'lon_dist_subobs_to_rearobs_2',\n",
       " 'ego_phy_x_2',\n",
       " 'ego_phy_y_2',\n",
       " 'ego_phy_vx_2',\n",
       " 'ego_phy_vy_2',\n",
       " 'ego_phy_yaw_2',\n",
       " 'obs_phy_x_2',\n",
       " 'obs_phy_y_2',\n",
       " 'obs_phy_vx_2',\n",
       " 'obs_phy_vy_2',\n",
       " 'obs_phy_yaw_2',\n",
       " 'target_front_obs_trackid_2',\n",
       " 'target_front_obs_uid_2',\n",
       " 'target_front_obs_chassis_x_2',\n",
       " 'target_front_obs_chassis_y_2',\n",
       " 'target_front_obs_chassis_vx_2',\n",
       " 'target_front_obs_chassis_vy_2',\n",
       " 'target_front_obs_chassis_yaw_2',\n",
       " 'target_front_obs_chassis_bx1_2',\n",
       " 'target_front_obs_chassis_bx2_2',\n",
       " 'target_front_obs_chassis_bx3_2',\n",
       " 'target_front_obs_chassis_bx4_2',\n",
       " 'target_front_obs_chassis_by1_2',\n",
       " 'target_front_obs_chassis_by2_2',\n",
       " 'target_front_obs_chassis_by3_2',\n",
       " 'target_front_obs_chassis_by4_2',\n",
       " 'target_front_obs_length_2',\n",
       " 'target_front_obs_width_2',\n",
       " 'target_front_obs_phy_yaw_2',\n",
       " 'target_front_obs_phy_x_2',\n",
       " 'target_front_obs_phy_y_2',\n",
       " 'target_front_obs_phy_vx_2',\n",
       " 'target_front_obs_phy_vy_2',\n",
       " 'target_rear_obs_trackid_2',\n",
       " 'target_rear_obs_uid_2',\n",
       " 'target_rear_obs_chassis_x_2',\n",
       " 'target_rear_obs_chassis_y_2',\n",
       " 'target_rear_obs_chassis_vx_2',\n",
       " 'target_rear_obs_chassis_vy_2',\n",
       " 'target_rear_obs_chassis_yaw_2',\n",
       " 'target_rear_obs_chassis_bx1_2',\n",
       " 'target_rear_obs_chassis_bx2_2',\n",
       " 'target_rear_obs_chassis_bx3_2',\n",
       " 'target_rear_obs_chassis_bx4_2',\n",
       " 'target_rear_obs_chassis_by1_2',\n",
       " 'target_rear_obs_chassis_by2_2',\n",
       " 'target_rear_obs_chassis_by3_2',\n",
       " 'target_rear_obs_chassis_by4_2',\n",
       " 'target_rear_obs_length_2',\n",
       " 'target_rear_obs_width_2',\n",
       " 'target_rear_obs_phy_yaw_2',\n",
       " 'target_rear_obs_phy_x_2',\n",
       " 'target_rear_obs_phy_y_2',\n",
       " 'target_rear_obs_phy_vx_2',\n",
       " 'target_rear_obs_phy_vy_2',\n",
       " 'front_obs_trackid_2',\n",
       " 'front_obs_uid_2',\n",
       " 'front_obs_chassis_x_2',\n",
       " 'front_obs_chassis_y_2',\n",
       " 'front_obs_chassis_vx_2',\n",
       " 'front_obs_chassis_vy_2',\n",
       " 'front_obs_chassis_yaw_2',\n",
       " 'front_obs_chassis_bx1_2',\n",
       " 'front_obs_chassis_bx2_2',\n",
       " 'front_obs_chassis_bx3_2',\n",
       " 'front_obs_chassis_bx4_2',\n",
       " 'front_obs_chassis_by1_2',\n",
       " 'front_obs_chassis_by2_2',\n",
       " 'front_obs_chassis_by3_2',\n",
       " 'front_obs_chassis_by4_2',\n",
       " 'front_obs_length_2',\n",
       " 'front_obs_width_2',\n",
       " 'front_obs_phy_yaw_2',\n",
       " 'front_obs_phy_x_2',\n",
       " 'front_obs_phy_y_2',\n",
       " 'front_obs_phy_vx_2',\n",
       " 'front_obs_phy_vy_2',\n",
       " 'rear_obs_trackid_2',\n",
       " 'rear_obs_uid_2',\n",
       " 'rear_obs_chassis_x_2',\n",
       " 'rear_obs_chassis_y_2',\n",
       " 'rear_obs_chassis_vx_2',\n",
       " 'rear_obs_chassis_vy_2',\n",
       " 'rear_obs_chassis_yaw_2',\n",
       " 'rear_obs_chassis_bx1_2',\n",
       " 'rear_obs_chassis_bx2_2',\n",
       " 'rear_obs_chassis_bx3_2',\n",
       " 'rear_obs_chassis_bx4_2',\n",
       " 'rear_obs_chassis_by1_2',\n",
       " 'rear_obs_chassis_by2_2',\n",
       " 'rear_obs_chassis_by3_2',\n",
       " 'rear_obs_chassis_by4_2',\n",
       " 'rear_obs_length_2',\n",
       " 'rear_obs_width_2',\n",
       " 'rear_obs_phy_yaw_2',\n",
       " 'rear_obs_phy_x_2',\n",
       " 'rear_obs_phy_y_2',\n",
       " 'rear_obs_phy_vx_2',\n",
       " 'rear_obs_phy_vy_2',\n",
       " 'idx_in_lane_2',\n",
       " 'stamp_3',\n",
       " 'id_3',\n",
       " 'uid_3',\n",
       " 'x_3',\n",
       " 'y_3',\n",
       " 'vx_3',\n",
       " 'vy_3',\n",
       " 'ax_3',\n",
       " 'ay_3',\n",
       " 'yaw_3',\n",
       " 'yaw_rate_3',\n",
       " 'length_3',\n",
       " 'width_3',\n",
       " 'bx1_3',\n",
       " 'by1_3',\n",
       " 'bx2_3',\n",
       " 'by2_3',\n",
       " 'bx3_3',\n",
       " 'by3_3',\n",
       " 'bx4_3',\n",
       " 'by4_3',\n",
       " 'image_left_3',\n",
       " 'image_top_3',\n",
       " 'image_right_3',\n",
       " 'image_bottom_3',\n",
       " 'property_type_3',\n",
       " 'property_name_3',\n",
       " 'turn_light_3',\n",
       " 'lat_dist_subobs_front_to_tarlaneline_3',\n",
       " 'lat_dist_subobs_rear_to_tarlaneline_3',\n",
       " 'lat_avg_dist_subobs_to_tarlaneline_3',\n",
       " 'lat_min_dist_subobs_to_tarlaneline_3',\n",
       " 'lat_dist_subobs_b1_to_tarlaneline_3',\n",
       " 'lat_dist_subobs_b2_to_tarlaneline_3',\n",
       " 'lat_dist_subobs_b3_to_tarlaneline_3',\n",
       " 'lat_dist_subobs_b4_to_tarlaneline_3',\n",
       " 'lon_dist_subobs_to_tarfrontobs_3',\n",
       " 'lon_dist_subobs_to_tarrearobs_3',\n",
       " 'lon_dist_subobs_to_frontobs_3',\n",
       " 'lon_dist_subobs_to_rearobs_3',\n",
       " 'ego_phy_x_3',\n",
       " 'ego_phy_y_3',\n",
       " 'ego_phy_vx_3',\n",
       " 'ego_phy_vy_3',\n",
       " 'ego_phy_yaw_3',\n",
       " 'obs_phy_x_3',\n",
       " 'obs_phy_y_3',\n",
       " 'obs_phy_vx_3',\n",
       " 'obs_phy_vy_3',\n",
       " 'obs_phy_yaw_3',\n",
       " 'target_front_obs_trackid_3',\n",
       " 'target_front_obs_uid_3',\n",
       " 'target_front_obs_chassis_x_3',\n",
       " 'target_front_obs_chassis_y_3',\n",
       " 'target_front_obs_chassis_vx_3',\n",
       " 'target_front_obs_chassis_vy_3',\n",
       " 'target_front_obs_chassis_yaw_3',\n",
       " 'target_front_obs_chassis_bx1_3',\n",
       " 'target_front_obs_chassis_bx2_3',\n",
       " 'target_front_obs_chassis_bx3_3',\n",
       " 'target_front_obs_chassis_bx4_3',\n",
       " 'target_front_obs_chassis_by1_3',\n",
       " 'target_front_obs_chassis_by2_3',\n",
       " 'target_front_obs_chassis_by3_3',\n",
       " 'target_front_obs_chassis_by4_3',\n",
       " 'target_front_obs_length_3',\n",
       " 'target_front_obs_width_3',\n",
       " 'target_front_obs_phy_yaw_3',\n",
       " 'target_front_obs_phy_x_3',\n",
       " 'target_front_obs_phy_y_3',\n",
       " 'target_front_obs_phy_vx_3',\n",
       " 'target_front_obs_phy_vy_3',\n",
       " 'target_rear_obs_trackid_3',\n",
       " 'target_rear_obs_uid_3',\n",
       " 'target_rear_obs_chassis_x_3',\n",
       " 'target_rear_obs_chassis_y_3',\n",
       " 'target_rear_obs_chassis_vx_3',\n",
       " 'target_rear_obs_chassis_vy_3',\n",
       " 'target_rear_obs_chassis_yaw_3',\n",
       " 'target_rear_obs_chassis_bx1_3',\n",
       " 'target_rear_obs_chassis_bx2_3',\n",
       " 'target_rear_obs_chassis_bx3_3',\n",
       " 'target_rear_obs_chassis_bx4_3',\n",
       " 'target_rear_obs_chassis_by1_3',\n",
       " 'target_rear_obs_chassis_by2_3',\n",
       " 'target_rear_obs_chassis_by3_3',\n",
       " 'target_rear_obs_chassis_by4_3',\n",
       " 'target_rear_obs_length_3',\n",
       " 'target_rear_obs_width_3',\n",
       " 'target_rear_obs_phy_yaw_3',\n",
       " 'target_rear_obs_phy_x_3',\n",
       " 'target_rear_obs_phy_y_3',\n",
       " 'target_rear_obs_phy_vx_3',\n",
       " 'target_rear_obs_phy_vy_3',\n",
       " 'front_obs_trackid_3',\n",
       " 'front_obs_uid_3',\n",
       " 'front_obs_chassis_x_3',\n",
       " 'front_obs_chassis_y_3',\n",
       " 'front_obs_chassis_vx_3',\n",
       " 'front_obs_chassis_vy_3',\n",
       " 'front_obs_chassis_yaw_3',\n",
       " 'front_obs_chassis_bx1_3',\n",
       " 'front_obs_chassis_bx2_3',\n",
       " 'front_obs_chassis_bx3_3',\n",
       " 'front_obs_chassis_bx4_3',\n",
       " 'front_obs_chassis_by1_3',\n",
       " 'front_obs_chassis_by2_3',\n",
       " 'front_obs_chassis_by3_3',\n",
       " 'front_obs_chassis_by4_3',\n",
       " 'front_obs_length_3',\n",
       " 'front_obs_width_3',\n",
       " 'front_obs_phy_yaw_3',\n",
       " 'front_obs_phy_x_3',\n",
       " 'front_obs_phy_y_3',\n",
       " 'front_obs_phy_vx_3',\n",
       " 'front_obs_phy_vy_3',\n",
       " 'rear_obs_trackid_3',\n",
       " 'rear_obs_uid_3',\n",
       " 'rear_obs_chassis_x_3',\n",
       " 'rear_obs_chassis_y_3',\n",
       " 'rear_obs_chassis_vx_3',\n",
       " 'rear_obs_chassis_vy_3',\n",
       " 'rear_obs_chassis_yaw_3',\n",
       " 'rear_obs_chassis_bx1_3',\n",
       " 'rear_obs_chassis_bx2_3',\n",
       " 'rear_obs_chassis_bx3_3',\n",
       " 'rear_obs_chassis_bx4_3',\n",
       " 'rear_obs_chassis_by1_3',\n",
       " 'rear_obs_chassis_by2_3',\n",
       " 'rear_obs_chassis_by3_3',\n",
       " 'rear_obs_chassis_by4_3',\n",
       " 'rear_obs_length_3',\n",
       " 'rear_obs_width_3',\n",
       " 'rear_obs_phy_yaw_3',\n",
       " 'rear_obs_phy_x_3',\n",
       " 'rear_obs_phy_y_3',\n",
       " 'rear_obs_phy_vx_3',\n",
       " 'rear_obs_phy_vy_3',\n",
       " 'idx_in_lane_3',\n",
       " 'stamp_4',\n",
       " 'id_4',\n",
       " 'uid_4',\n",
       " 'x_4',\n",
       " 'y_4',\n",
       " 'vx_4',\n",
       " 'vy_4',\n",
       " 'ax_4',\n",
       " 'ay_4',\n",
       " 'yaw_4',\n",
       " 'yaw_rate_4',\n",
       " 'length_4',\n",
       " 'width_4',\n",
       " 'bx1_4',\n",
       " 'by1_4',\n",
       " 'bx2_4',\n",
       " 'by2_4',\n",
       " 'bx3_4',\n",
       " 'by3_4',\n",
       " 'bx4_4',\n",
       " 'by4_4',\n",
       " 'image_left_4',\n",
       " 'image_top_4',\n",
       " 'image_right_4',\n",
       " 'image_bottom_4',\n",
       " 'property_type_4',\n",
       " 'property_name_4',\n",
       " 'turn_light_4',\n",
       " 'lat_dist_subobs_front_to_tarlaneline_4',\n",
       " 'lat_dist_subobs_rear_to_tarlaneline_4',\n",
       " 'lat_avg_dist_subobs_to_tarlaneline_4',\n",
       " 'lat_min_dist_subobs_to_tarlaneline_4',\n",
       " 'lat_dist_subobs_b1_to_tarlaneline_4',\n",
       " 'lat_dist_subobs_b2_to_tarlaneline_4',\n",
       " 'lat_dist_subobs_b3_to_tarlaneline_4',\n",
       " 'lat_dist_subobs_b4_to_tarlaneline_4',\n",
       " 'lon_dist_subobs_to_tarfrontobs_4',\n",
       " 'lon_dist_subobs_to_tarrearobs_4',\n",
       " 'lon_dist_subobs_to_frontobs_4',\n",
       " 'lon_dist_subobs_to_rearobs_4',\n",
       " 'ego_phy_x_4',\n",
       " 'ego_phy_y_4',\n",
       " 'ego_phy_vx_4',\n",
       " 'ego_phy_vy_4',\n",
       " 'ego_phy_yaw_4',\n",
       " 'obs_phy_x_4',\n",
       " 'obs_phy_y_4',\n",
       " 'obs_phy_vx_4',\n",
       " 'obs_phy_vy_4',\n",
       " 'obs_phy_yaw_4',\n",
       " 'target_front_obs_trackid_4',\n",
       " 'target_front_obs_uid_4',\n",
       " 'target_front_obs_chassis_x_4',\n",
       " 'target_front_obs_chassis_y_4',\n",
       " 'target_front_obs_chassis_vx_4',\n",
       " 'target_front_obs_chassis_vy_4',\n",
       " 'target_front_obs_chassis_yaw_4',\n",
       " 'target_front_obs_chassis_bx1_4',\n",
       " 'target_front_obs_chassis_bx2_4',\n",
       " 'target_front_obs_chassis_bx3_4',\n",
       " 'target_front_obs_chassis_bx4_4',\n",
       " 'target_front_obs_chassis_by1_4',\n",
       " 'target_front_obs_chassis_by2_4',\n",
       " 'target_front_obs_chassis_by3_4',\n",
       " 'target_front_obs_chassis_by4_4',\n",
       " 'target_front_obs_length_4',\n",
       " 'target_front_obs_width_4',\n",
       " 'target_front_obs_phy_yaw_4',\n",
       " 'target_front_obs_phy_x_4',\n",
       " 'target_front_obs_phy_y_4',\n",
       " 'target_front_obs_phy_vx_4',\n",
       " 'target_front_obs_phy_vy_4',\n",
       " 'target_rear_obs_trackid_4',\n",
       " 'target_rear_obs_uid_4',\n",
       " 'target_rear_obs_chassis_x_4',\n",
       " 'target_rear_obs_chassis_y_4',\n",
       " 'target_rear_obs_chassis_vx_4',\n",
       " 'target_rear_obs_chassis_vy_4',\n",
       " 'target_rear_obs_chassis_yaw_4',\n",
       " 'target_rear_obs_chassis_bx1_4',\n",
       " 'target_rear_obs_chassis_bx2_4',\n",
       " 'target_rear_obs_chassis_bx3_4',\n",
       " 'target_rear_obs_chassis_bx4_4',\n",
       " 'target_rear_obs_chassis_by1_4',\n",
       " 'target_rear_obs_chassis_by2_4',\n",
       " 'target_rear_obs_chassis_by3_4',\n",
       " 'target_rear_obs_chassis_by4_4',\n",
       " 'target_rear_obs_length_4',\n",
       " 'target_rear_obs_width_4',\n",
       " 'target_rear_obs_phy_yaw_4',\n",
       " 'target_rear_obs_phy_x_4',\n",
       " 'target_rear_obs_phy_y_4',\n",
       " 'target_rear_obs_phy_vx_4',\n",
       " 'target_rear_obs_phy_vy_4',\n",
       " 'front_obs_trackid_4',\n",
       " 'front_obs_uid_4',\n",
       " 'front_obs_chassis_x_4',\n",
       " 'front_obs_chassis_y_4',\n",
       " 'front_obs_chassis_vx_4',\n",
       " 'front_obs_chassis_vy_4',\n",
       " 'front_obs_chassis_yaw_4',\n",
       " 'front_obs_chassis_bx1_4',\n",
       " 'front_obs_chassis_bx2_4',\n",
       " 'front_obs_chassis_bx3_4',\n",
       " 'front_obs_chassis_bx4_4',\n",
       " 'front_obs_chassis_by1_4',\n",
       " 'front_obs_chassis_by2_4',\n",
       " 'front_obs_chassis_by3_4',\n",
       " 'front_obs_chassis_by4_4',\n",
       " 'front_obs_length_4',\n",
       " 'front_obs_width_4',\n",
       " 'front_obs_phy_yaw_4',\n",
       " 'front_obs_phy_x_4',\n",
       " 'front_obs_phy_y_4',\n",
       " 'front_obs_phy_vx_4',\n",
       " 'front_obs_phy_vy_4',\n",
       " 'rear_obs_trackid_4',\n",
       " 'rear_obs_uid_4',\n",
       " 'rear_obs_chassis_x_4',\n",
       " 'rear_obs_chassis_y_4',\n",
       " 'rear_obs_chassis_vx_4',\n",
       " 'rear_obs_chassis_vy_4',\n",
       " 'rear_obs_chassis_yaw_4',\n",
       " 'rear_obs_chassis_bx1_4',\n",
       " 'rear_obs_chassis_bx2_4',\n",
       " 'rear_obs_chassis_bx3_4',\n",
       " 'rear_obs_chassis_bx4_4',\n",
       " 'rear_obs_chassis_by1_4',\n",
       " 'rear_obs_chassis_by2_4',\n",
       " 'rear_obs_chassis_by3_4',\n",
       " 'rear_obs_chassis_by4_4',\n",
       " 'rear_obs_length_4',\n",
       " 'rear_obs_width_4',\n",
       " 'rear_obs_phy_yaw_4',\n",
       " 'rear_obs_phy_x_4',\n",
       " 'rear_obs_phy_y_4',\n",
       " 'rear_obs_phy_vx_4',\n",
       " 'rear_obs_phy_vy_4',\n",
       " 'idx_in_lane_4',\n",
       " 'stamp_5',\n",
       " 'id_5',\n",
       " 'uid_5',\n",
       " 'x_5',\n",
       " 'y_5',\n",
       " 'vx_5',\n",
       " 'vy_5',\n",
       " 'ax_5',\n",
       " 'ay_5',\n",
       " 'yaw_5',\n",
       " 'yaw_rate_5',\n",
       " 'length_5',\n",
       " 'width_5',\n",
       " 'bx1_5',\n",
       " 'by1_5',\n",
       " 'bx2_5',\n",
       " 'by2_5',\n",
       " 'bx3_5',\n",
       " 'by3_5',\n",
       " 'bx4_5',\n",
       " 'by4_5',\n",
       " 'image_left_5',\n",
       " 'image_top_5',\n",
       " 'image_right_5',\n",
       " 'image_bottom_5',\n",
       " 'property_type_5',\n",
       " 'property_name_5',\n",
       " 'turn_light_5',\n",
       " 'lat_dist_subobs_front_to_tarlaneline_5',\n",
       " 'lat_dist_subobs_rear_to_tarlaneline_5',\n",
       " 'lat_avg_dist_subobs_to_tarlaneline_5',\n",
       " 'lat_min_dist_subobs_to_tarlaneline_5',\n",
       " 'lat_dist_subobs_b1_to_tarlaneline_5',\n",
       " 'lat_dist_subobs_b2_to_tarlaneline_5',\n",
       " 'lat_dist_subobs_b3_to_tarlaneline_5',\n",
       " 'lat_dist_subobs_b4_to_tarlaneline_5',\n",
       " 'lon_dist_subobs_to_tarfrontobs_5',\n",
       " 'lon_dist_subobs_to_tarrearobs_5',\n",
       " 'lon_dist_subobs_to_frontobs_5',\n",
       " 'lon_dist_subobs_to_rearobs_5',\n",
       " 'ego_phy_x_5',\n",
       " 'ego_phy_y_5',\n",
       " 'ego_phy_vx_5',\n",
       " 'ego_phy_vy_5',\n",
       " 'ego_phy_yaw_5',\n",
       " 'obs_phy_x_5',\n",
       " 'obs_phy_y_5',\n",
       " 'obs_phy_vx_5',\n",
       " 'obs_phy_vy_5',\n",
       " 'obs_phy_yaw_5',\n",
       " 'target_front_obs_trackid_5',\n",
       " 'target_front_obs_uid_5',\n",
       " 'target_front_obs_chassis_x_5',\n",
       " 'target_front_obs_chassis_y_5',\n",
       " 'target_front_obs_chassis_vx_5',\n",
       " 'target_front_obs_chassis_vy_5',\n",
       " 'target_front_obs_chassis_yaw_5',\n",
       " 'target_front_obs_chassis_bx1_5',\n",
       " 'target_front_obs_chassis_bx2_5',\n",
       " 'target_front_obs_chassis_bx3_5',\n",
       " 'target_front_obs_chassis_bx4_5',\n",
       " 'target_front_obs_chassis_by1_5',\n",
       " 'target_front_obs_chassis_by2_5',\n",
       " 'target_front_obs_chassis_by3_5',\n",
       " 'target_front_obs_chassis_by4_5',\n",
       " 'target_front_obs_length_5',\n",
       " 'target_front_obs_width_5',\n",
       " 'target_front_obs_phy_yaw_5',\n",
       " 'target_front_obs_phy_x_5',\n",
       " 'target_front_obs_phy_y_5',\n",
       " 'target_front_obs_phy_vx_5',\n",
       " 'target_front_obs_phy_vy_5',\n",
       " 'target_rear_obs_trackid_5',\n",
       " 'target_rear_obs_uid_5',\n",
       " 'target_rear_obs_chassis_x_5',\n",
       " 'target_rear_obs_chassis_y_5',\n",
       " 'target_rear_obs_chassis_vx_5',\n",
       " 'target_rear_obs_chassis_vy_5',\n",
       " 'target_rear_obs_chassis_yaw_5',\n",
       " 'target_rear_obs_chassis_bx1_5',\n",
       " 'target_rear_obs_chassis_bx2_5',\n",
       " 'target_rear_obs_chassis_bx3_5',\n",
       " 'target_rear_obs_chassis_bx4_5',\n",
       " 'target_rear_obs_chassis_by1_5',\n",
       " 'target_rear_obs_chassis_by2_5',\n",
       " 'target_rear_obs_chassis_by3_5',\n",
       " 'target_rear_obs_chassis_by4_5',\n",
       " 'target_rear_obs_length_5',\n",
       " 'target_rear_obs_width_5',\n",
       " 'target_rear_obs_phy_yaw_5',\n",
       " 'target_rear_obs_phy_x_5',\n",
       " 'target_rear_obs_phy_y_5',\n",
       " 'target_rear_obs_phy_vx_5',\n",
       " 'target_rear_obs_phy_vy_5',\n",
       " 'front_obs_trackid_5',\n",
       " 'front_obs_uid_5',\n",
       " 'front_obs_chassis_x_5',\n",
       " 'front_obs_chassis_y_5',\n",
       " 'front_obs_chassis_vx_5',\n",
       " 'front_obs_chassis_vy_5',\n",
       " 'front_obs_chassis_yaw_5',\n",
       " 'front_obs_chassis_bx1_5',\n",
       " 'front_obs_chassis_bx2_5',\n",
       " 'front_obs_chassis_bx3_5',\n",
       " 'front_obs_chassis_bx4_5',\n",
       " 'front_obs_chassis_by1_5',\n",
       " 'front_obs_chassis_by2_5',\n",
       " 'front_obs_chassis_by3_5',\n",
       " 'front_obs_chassis_by4_5',\n",
       " 'front_obs_length_5',\n",
       " 'front_obs_width_5',\n",
       " 'front_obs_phy_yaw_5',\n",
       " 'front_obs_phy_x_5',\n",
       " 'front_obs_phy_y_5',\n",
       " 'front_obs_phy_vx_5',\n",
       " 'front_obs_phy_vy_5',\n",
       " 'rear_obs_trackid_5',\n",
       " 'rear_obs_uid_5',\n",
       " 'rear_obs_chassis_x_5',\n",
       " 'rear_obs_chassis_y_5',\n",
       " 'rear_obs_chassis_vx_5',\n",
       " 'rear_obs_chassis_vy_5',\n",
       " 'rear_obs_chassis_yaw_5',\n",
       " 'rear_obs_chassis_bx1_5',\n",
       " 'rear_obs_chassis_bx2_5',\n",
       " 'rear_obs_chassis_bx3_5',\n",
       " 'rear_obs_chassis_bx4_5',\n",
       " 'rear_obs_chassis_by1_5',\n",
       " 'rear_obs_chassis_by2_5',\n",
       " 'rear_obs_chassis_by3_5',\n",
       " 'rear_obs_chassis_by4_5',\n",
       " 'rear_obs_length_5',\n",
       " 'rear_obs_width_5',\n",
       " 'rear_obs_phy_yaw_5',\n",
       " 'rear_obs_phy_x_5',\n",
       " 'rear_obs_phy_y_5',\n",
       " 'rear_obs_phy_vx_5',\n",
       " 'rear_obs_phy_vy_5',\n",
       " 'idx_in_lane_5',\n",
       " 'stamp_6',\n",
       " 'id_6',\n",
       " 'uid_6',\n",
       " 'x_6',\n",
       " 'y_6',\n",
       " 'vx_6',\n",
       " 'vy_6',\n",
       " 'ax_6',\n",
       " 'ay_6',\n",
       " 'yaw_6',\n",
       " 'yaw_rate_6',\n",
       " 'length_6',\n",
       " 'width_6',\n",
       " 'bx1_6',\n",
       " 'by1_6',\n",
       " 'bx2_6',\n",
       " 'by2_6',\n",
       " 'bx3_6',\n",
       " 'by3_6',\n",
       " 'bx4_6',\n",
       " 'by4_6',\n",
       " 'image_left_6',\n",
       " 'image_top_6',\n",
       " 'image_right_6',\n",
       " 'image_bottom_6',\n",
       " 'property_type_6',\n",
       " 'property_name_6',\n",
       " 'turn_light_6',\n",
       " 'lat_dist_subobs_front_to_tarlaneline_6',\n",
       " 'lat_dist_subobs_rear_to_tarlaneline_6',\n",
       " 'lat_avg_dist_subobs_to_tarlaneline_6',\n",
       " 'lat_min_dist_subobs_to_tarlaneline_6',\n",
       " 'lat_dist_subobs_b1_to_tarlaneline_6',\n",
       " 'lat_dist_subobs_b2_to_tarlaneline_6',\n",
       " 'lat_dist_subobs_b3_to_tarlaneline_6',\n",
       " 'lat_dist_subobs_b4_to_tarlaneline_6',\n",
       " 'lon_dist_subobs_to_tarfrontobs_6',\n",
       " 'lon_dist_subobs_to_tarrearobs_6',\n",
       " 'lon_dist_subobs_to_frontobs_6',\n",
       " 'lon_dist_subobs_to_rearobs_6',\n",
       " 'ego_phy_x_6',\n",
       " 'ego_phy_y_6',\n",
       " 'ego_phy_vx_6',\n",
       " 'ego_phy_vy_6',\n",
       " 'ego_phy_yaw_6',\n",
       " 'obs_phy_x_6',\n",
       " 'obs_phy_y_6',\n",
       " 'obs_phy_vx_6',\n",
       " 'obs_phy_vy_6',\n",
       " 'obs_phy_yaw_6',\n",
       " 'target_front_obs_trackid_6',\n",
       " 'target_front_obs_uid_6',\n",
       " 'target_front_obs_chassis_x_6',\n",
       " 'target_front_obs_chassis_y_6',\n",
       " 'target_front_obs_chassis_vx_6',\n",
       " 'target_front_obs_chassis_vy_6',\n",
       " 'target_front_obs_chassis_yaw_6',\n",
       " 'target_front_obs_chassis_bx1_6',\n",
       " 'target_front_obs_chassis_bx2_6',\n",
       " 'target_front_obs_chassis_bx3_6',\n",
       " 'target_front_obs_chassis_bx4_6',\n",
       " 'target_front_obs_chassis_by1_6',\n",
       " 'target_front_obs_chassis_by2_6',\n",
       " 'target_front_obs_chassis_by3_6',\n",
       " 'target_front_obs_chassis_by4_6',\n",
       " 'target_front_obs_length_6',\n",
       " 'target_front_obs_width_6',\n",
       " 'target_front_obs_phy_yaw_6',\n",
       " 'target_front_obs_phy_x_6',\n",
       " 'target_front_obs_phy_y_6',\n",
       " 'target_front_obs_phy_vx_6',\n",
       " 'target_front_obs_phy_vy_6',\n",
       " 'target_rear_obs_trackid_6',\n",
       " 'target_rear_obs_uid_6',\n",
       " 'target_rear_obs_chassis_x_6',\n",
       " 'target_rear_obs_chassis_y_6',\n",
       " 'target_rear_obs_chassis_vx_6',\n",
       " 'target_rear_obs_chassis_vy_6',\n",
       " 'target_rear_obs_chassis_yaw_6',\n",
       " 'target_rear_obs_chassis_bx1_6',\n",
       " 'target_rear_obs_chassis_bx2_6',\n",
       " 'target_rear_obs_chassis_bx3_6',\n",
       " 'target_rear_obs_chassis_bx4_6',\n",
       " 'target_rear_obs_chassis_by1_6',\n",
       " 'target_rear_obs_chassis_by2_6',\n",
       " 'target_rear_obs_chassis_by3_6',\n",
       " 'target_rear_obs_chassis_by4_6',\n",
       " 'target_rear_obs_length_6',\n",
       " 'target_rear_obs_width_6',\n",
       " 'target_rear_obs_phy_yaw_6',\n",
       " 'target_rear_obs_phy_x_6',\n",
       " 'target_rear_obs_phy_y_6',\n",
       " 'target_rear_obs_phy_vx_6',\n",
       " 'target_rear_obs_phy_vy_6',\n",
       " 'front_obs_trackid_6',\n",
       " 'front_obs_uid_6',\n",
       " 'front_obs_chassis_x_6',\n",
       " 'front_obs_chassis_y_6',\n",
       " 'front_obs_chassis_vx_6',\n",
       " 'front_obs_chassis_vy_6',\n",
       " 'front_obs_chassis_yaw_6',\n",
       " 'front_obs_chassis_bx1_6',\n",
       " 'front_obs_chassis_bx2_6',\n",
       " 'front_obs_chassis_bx3_6',\n",
       " 'front_obs_chassis_bx4_6',\n",
       " 'front_obs_chassis_by1_6',\n",
       " 'front_obs_chassis_by2_6',\n",
       " 'front_obs_chassis_by3_6',\n",
       " 'front_obs_chassis_by4_6',\n",
       " 'front_obs_length_6',\n",
       " 'front_obs_width_6',\n",
       " 'front_obs_phy_yaw_6',\n",
       " 'front_obs_phy_x_6',\n",
       " 'front_obs_phy_y_6',\n",
       " 'front_obs_phy_vx_6',\n",
       " 'front_obs_phy_vy_6',\n",
       " 'rear_obs_trackid_6',\n",
       " 'rear_obs_uid_6',\n",
       " 'rear_obs_chassis_x_6',\n",
       " 'rear_obs_chassis_y_6',\n",
       " 'rear_obs_chassis_vx_6',\n",
       " 'rear_obs_chassis_vy_6',\n",
       " 'rear_obs_chassis_yaw_6',\n",
       " 'rear_obs_chassis_bx1_6',\n",
       " 'rear_obs_chassis_bx2_6',\n",
       " 'rear_obs_chassis_bx3_6',\n",
       " 'rear_obs_chassis_bx4_6',\n",
       " 'rear_obs_chassis_by1_6',\n",
       " 'rear_obs_chassis_by2_6',\n",
       " 'rear_obs_chassis_by3_6',\n",
       " 'rear_obs_chassis_by4_6',\n",
       " 'rear_obs_length_6',\n",
       " 'rear_obs_width_6',\n",
       " 'rear_obs_phy_yaw_6',\n",
       " 'rear_obs_phy_x_6',\n",
       " 'rear_obs_phy_y_6',\n",
       " 'rear_obs_phy_vx_6',\n",
       " 'rear_obs_phy_vy_6',\n",
       " 'idx_in_lane_6',\n",
       " 'stamp_7',\n",
       " 'id_7',\n",
       " 'uid_7',\n",
       " ...]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['20220304-111358_356_6', '']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exist_sample = os.listdir(\"/mnt/data-3/huajiang.liu/BADCASE/output/sample\")\n",
    "exist_sample[0].split(\"_sample.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20220304-111358_356_6_sample.csv'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exist_sample[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 44, -16,  -9,  31,  44]),\n",
       " array([ -40,  -40,  -40, -280]),\n",
       " array([-100,  -33,    0, -267]))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.array([1646366297823,1646366297863,1646366297903,1646366297943,1646366298223])\n",
    "b=np.array([1646366297779,1646366297879,1646366297912,1646366297912,1646366298179])\n",
    "a-b,a[:-1]-a[1:],b[:-1]-b[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def draw_lane(coeffs, start_x, end_x):\n",
    "    xs = []\n",
    "    ys = []\n",
    "    for x in np.arange(start_x, end_x, 1):\n",
    "        y = coeffs[3]*x**3 + coeffs[2]*x**2 + coeffs[1]*x +  coeffs[0]\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    \n",
    "    plt.scatter(ys,xs,s=1)\n",
    "    plt.xlim(10, -10)\n",
    "    plt.ylim(-50, 100)\n",
    "    xticks = np.arange(-10,10,1)\n",
    "    yticks = np.arange(-50,100,1)\n",
    "    plt.xticks(xticks)\n",
    "    plt.yticks(yticks)\n",
    "plt.figure(figsize=(2,10))\n",
    "\n",
    "coeffs1= np.array([1.97263,0.00638774,4.0222e-05,2.8942e-07])\n",
    "draw_lane(coeffs1,5.58455,97.6783)\n",
    "\n",
    "coeffs1= np.array([-5.99768,0.00578706,4.02052e-05,2.8942e-07])\n",
    "draw_lane(coeffs1,5.57468,97.6684)\n",
    "\n",
    "coeffs1= np.array([5.79501,0.0064159,4.02157e-05,2.8942e-07])\n",
    "draw_lane(coeffs1,5.59419 ,97.6684)\n",
    "\n",
    "coeffs1= np.array([-2.02841,0.00581606,4.02115e-05,2.8942e-07])\n",
    "draw_lane(coeffs1,5.59419 ,97.6684)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for &: 'str' and 'bool'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-51-b28666be5f88>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"./\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mpn\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for &: 'str' and 'bool'"
     ]
    }
   ],
   "source": [
    "pn=\"./\"\n",
    "if pn:\n",
    "    if os.path.exists(pn):\n",
    "        print(\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "CPU0 = []\n",
    "CPU1 = []\n",
    "CPU2 = []\n",
    "CPU3 = []\n",
    "f = open(\"/home/users/huajiang.liu/codes/tmp/V7.0.3_4core.txt\",\"r\")   #设置文件对象\n",
    "line = f.readline()\n",
    "while line:           #直到读取完文件\n",
    "    line = f.readline()  #读取一行文件，包括换行符\n",
    "    line = line[:-1]     #去掉换行符，也可以不去\n",
    "    if \"CPU0:\" in line:\n",
    "        cost0 = float(line.split(\"CPU0:\")[1].split(\"%\")[0])\n",
    "        CPU0.append(cost0)\n",
    "    if \"CPU1:\" in line:\n",
    "        cost1 = float(line.split(\"CPU1:\")[1].split(\"%\")[0])\n",
    "        CPU1.append(cost1)\n",
    "    if \"CPU2:\" in line:\n",
    "        cost2 = float(line.split(\"CPU2:\")[1].split(\"%\")[0])\n",
    "        CPU2.append(cost2) \n",
    "    if \"CPU3:\" in line:\n",
    "        cost3 = float(line.split(\"CPU3:\")[1].split(\"%\")[0])\n",
    "        CPU3.append(cost3)   \n",
    "f.close() #关闭文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56.50637583892617,\n",
       " 57.20234899328859,\n",
       " 61.290939597315436,\n",
       " 62.81174496644296,\n",
       " 59.45285234899329)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "CPU0 = np.array(CPU0)\n",
    "CPU1 = np.array(CPU1)\n",
    "CPU2 = np.array(CPU2)\n",
    "CPU3 = np.array(CPU3)\n",
    "# obs_ts_diff = obs_ts_diff[obs_ts_diff<500]\n",
    "# fig = plt.figure(figsize=(20,10))\n",
    "x = [i for i in range(len(CPU0))]\n",
    "y = CPU0\n",
    "plt.plot([i for i in range(len(CPU0))],CPU0,c=\"b\",label=\"cpu0\")\n",
    "plt.plot([i for i in range(len(CPU1))],CPU1,c=\"g\",label=\"cpu1\")\n",
    "plt.plot([i for i in range(len(CPU2))],CPU2,c=\"y\",label=\"cpu2\")\n",
    "plt.plot([i for i in range(len(CPU3))],CPU3,c=\"r\",label=\"cpu3\")\n",
    "plt.legend()\n",
    "CPU0.mean(),CPU1.mean(),CPU2.mean(),CPU3.mean(),np.array([CPU0.mean(),CPU1.mean(),CPU2.mean(),CPU3.mean()]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_ts_diff = []\n",
    "f = open(\"/home/users/huajiang.liu/codes/noa/log/_20220315-150926-932_log20220315-150926.15304\",\"r\")   #设置文件对象\n",
    "line = f.readline()\n",
    "while line:           #直到读取完文件\n",
    "    line = f.readline()  #读取一行文件，包括换行符\n",
    "    line = line[:-1]     #去掉换行符，也可以不去\n",
    "    if \"vehicle_stamp\" in line:\n",
    "        vehicle_stamp = line.split(\"vehicle_stamp\")\n",
    "    if \"Curvarture K\" in line:\n",
    "        Curvarture = line.split(\":\")\n",
    "        obs_ts_diff.append([float(Curvarture[-1]), int(vehicle_stamp[-1])])\n",
    "f.close() #关闭文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa1ffa96f98>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "obs_ts_diff = np.array(obs_ts_diff)\n",
    "# obs_ts_diff = obs_ts_diff[obs_ts_diff<500]\n",
    "fig=plt.figure()\n",
    "x = obs_ts_diff[:,1]\n",
    "y = obs_ts_diff[:,0]\n",
    "plt.scatter(x,y,s=1,c=\"r\",label=\"obs_ts_diff\")\n",
    "fig.suptitle(\"mono-lane & 6v-obs\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-122"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1646366210463-1646366210585"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-8-f18332450da5>, line 15)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-8-f18332450da5>\"\u001b[0;36m, line \u001b[0;32m15\u001b[0m\n\u001b[0;31m    i + = 1\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "obs_ts = []\n",
    "lane_ts = []\n",
    "f = open(\"/home/users/huajiang.liu/codes/noa/log/_20220309-112210-939_log20220309-112210.3505\",\"r\")   #设置文件对象\n",
    "line = f.readline()\n",
    "i = 0\n",
    "while line:           #直到读取完文件\n",
    "    line = f.readline()  #读取一行文件，包括换行符\n",
    "    line = line[:-1]     #去掉换行符，也可以不去\n",
    "    if \"obs-ts\" in line:\n",
    "        line = line.split(\":\")\n",
    "        obs_ts.append(int(line[-1]))\n",
    "    if \"lane-ts\" in line:\n",
    "        line = line.split(\":\")\n",
    "        lane_ts.append(int(line[-1]))\n",
    "    i + = 1\n",
    "f.close() #关闭文件\n",
    "obs_ts=np.array(obs_ts)\n",
    "lane_ts=np.array(lane_ts)\n",
    "\n",
    "# draw image\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "x_major_locator=MultipleLocator(66)\n",
    "\n",
    "def return_mask(array):\n",
    "    mask1 = array > (1646366298012 - 5000)\n",
    "    mask2 = array < (1646366298012 + 5000)\n",
    "    return mask1 & mask2\n",
    "fig=plt.figure(figsize=(24,1))\n",
    "obs_ts = obs_ts[return_mask(obs_ts)]\n",
    "lane_ts = lane_ts[return_mask(lane_ts)]\n",
    "y1 = np.array([1 for  i in range(len(obs_ts))])\n",
    "y2 = np.array([0 for i in range(len(lane_ts))])\n",
    "x1 = obs_ts\n",
    "x2 = lane_ts\n",
    "ax=plt.gca()\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "plt.scatter(x1,y1,s=1,c=\"r\",label=\"obs_ts\")\n",
    "plt.scatter(x2,y2,s=1,c=\"black\",label=\"lane_ts\")\n",
    "fig.suptitle(\"mono-lane & 6v-obs\")\n",
    "# plt.grid(axis=\"y\")\n",
    "plt.grid(axis=\"x\")\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1646366293045, 1646366293778, 1646366294412, 1646366294412,\n",
       "       1646366294612, 1646366294612, 1646366294645, 1646366295012,\n",
       "       1646366295612, 1646366295612, 1646366296412, 1646366297612,\n",
       "       1646366297612, 1646366297879, 1646366297879, 1646366297879,\n",
       "       1646366297879, 1646366298012, 1646366298279, 1646366302979,\n",
       "       1646366302979, 1646366302979, 1646366302979])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lane_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import plot,savefig\n",
    "\n",
    "cutin_dist = []\n",
    "pre_cutin_dist = []\n",
    "timestamp=[]\n",
    "f = open(\"/home/users/huajiang.liu/codes/noa/log/_20220418-190946-771_log20220418-190946.2323748\",\"r\")   #设置文件对象\n",
    "line = f.readline()\n",
    "i = 0\n",
    "time_stamp = 0\n",
    "while line:           #直到读取完文件\n",
    "    line = f.readline()  #读取一行文件，包括换行符\n",
    "    line = line[:-1]     #去掉换行符，也可以不去\n",
    "#     if \"lane_change_predictor_interface.cpp:1796] time_stamp\" in line:\n",
    "#         time_stamp = int(line.split(\":\")[-1])\n",
    "#         timestamp.append(timestamp)\n",
    "    if \"cutin_dist\" in line:\n",
    "        dist = line.split(\":\")[-1].split(\"pre_cutin_dist\")\n",
    "        cutin_dist.append(float(dist[0]))\n",
    "        pre_cutin_dist.append(float(dist[1]))\n",
    "        timestamp.append(time_stamp)\n",
    "        time_stamp+=1\n",
    "#         obs_ts.append([int(line[-1]),time_stamp])\n",
    "#         i +=1\n",
    "#     if \"lane-ts\" in line:\n",
    "#         line = line.split(\":\")\n",
    "#         lane_ts.append([int(line[-1]),time_stamp])\n",
    "#         i += 1\n",
    "f.close() #关闭文件\n",
    "cutin_dist=np.array(cutin_dist)\n",
    "pre_cutin_dist=np.array(pre_cutin_dist)\n",
    "diff = pre_cutin_dist-cutin_dist\n",
    "timestamp = np.array(timestamp[0:len(cutin_dist)])\n",
    "zero = [0 for _ in range(len(cutin_dist))]\n",
    "# draw image\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "y_major_locator=MultipleLocator(0.1)\n",
    "x_major_locator=MultipleLocator(10)\n",
    "fig=plt.figure(figsize=(10,6))\n",
    "\n",
    "ax=plt.gca()\n",
    "ax.yaxis.set_major_locator(y_major_locator)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "# plt.scatter(timestamp,cutin_dist,s=1,c=\"r\",label=\"cutin_dist\")\n",
    "# plt.scatter(timestamp, pre_cutin_dist,s=1,c=\"black\",label=\"pre_cutin_dist\")\n",
    "# plt.scatter(timestamp, diff,s=1,c=\"b\",label=\"diff\")\n",
    "plt.plot(timestamp,cutin_dist,c=\"r\",label=\"cutin_dist\")\n",
    "plt.plot(timestamp, pre_cutin_dist,c=\"black\",label=\"pre_cutin_dist\")\n",
    "plt.plot(timestamp, diff,c=\"b\",label=\"diff\")\n",
    "fig.suptitle(\"cutin_dist & pre_cutin_dist\")\n",
    "plt.grid(axis=\"y\")\n",
    "plt.grid(axis=\"x\")\n",
    "plt.legend() \n",
    "savefig(\"./a.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
